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- [Narrator] Welcome to Nobel Conversations.
In this episode,
Josh Angrist and Guido Imbens sit down with Isaiah Andrews
to discuss how the field of econometrics is evolving.
- [Isaiah] So Guido and Josh, you're both pioneers
in developing tools for empirical research in economics.
And so I'd like to explore
sort of where you feel like the field is heading,
sort of economics, econometrics, the whole thing.
To start, I'd be interested to hear
about whether you feel like sort of the way in which
the local average treatment effects framework sort of took hold
has any lessons for how new empirical methods in economics
develop and spread or how they should.
- [Josh] That's a good question.
You go first.
(laughter)
Yeah, so I think the important thing
is to come up with good convincing cases
where the questions are clear
and where kind of the methods apply in general.
So one thing I--
Kind of looking back at the subsequent literature,
so I really like the regression discontinuity literature
[where there were] clearly a bunch of really convincing examples
and that allowed people to kind of
think more clearly, look harder at the methodological questions.
Kind of do clear applications
that then allow you to kind of think about,
"Wow, do this type of assumption seem reasonable here?
What kind of things do we not like in the early papers?
How can we improve things?"
So having clear applications motivating,
these literatures, I think it's very helpful.
I'm glad you mentioned the regression discontinuity, Guido.
I think there's a lot of complementarity between IV and RD,
Instrumental Variables and Regression Discontinuity.
And a lot of the econometric applications
of regression discontinuity
are what used to be called "fuzzy" RD,
where, you know, it's not discrete or deterministic at the cutoff,
but just the change in rates or intensity.
And and the late framework helps us understand those applications
and gives us a clear interpretation
for say, something like, in my paper with Victor Lavy,
where we use Maimonides' rule, the class size cutoffs.
What are you getting there?
Of course, you can answer that question
with a linear constant effects model,
but it turns out we're not limited to that,
and an RD is still very powerful and illuminating,
even when, you know,
the correlation between the cutoff and the variable of interest,
in this case class size, is partial,
maybe even not that strong.
So there was definitely a kind of, a parallel development.
It's also interesting,
you know, nobody talked about regression discontinuity designs
when we were in graduate school,
it was something that other social scientists were interested in,
and that kind of grew up alongside the late framework
and we've both done work on both applications and methods there
and it's been very exciting to see that kind of develop
and become so important.
It's part of a general evolution, I think, towards, you know,
credible identification strategies causal effects...
less, you know, making econometrics
more about causal questions than about models.
In terms of the future, I think one thing that LATE has helped facilitate
is a move towards more creative randomized, trials, where,
you know, there's something of interest,
it's not possible or straightforward to Simply turn it off or on
but you can encourage it
or discourage it. So, you subsidize schooling with financial aid, for example,
so now we have a whole
Framework for interpreting that.
And,
and it kind of opens the doors to randomized,
Trials of things that that maybe would,
you know,
not have seen possible before we've,
we've used that a lot in the work. We do on schools in our in the blueprint Lab
at MIT were exploiting random assignment and in very creative ways, I think.
Related to that. Do you see sort of particular factors that make for
useful research and econometrics.
You've alluded to it?
Having a clear connection to problems that are actually coming up.
And empirical practice is often a good idea. I'll send it. Always a good idea.
I often find myself sitting in an economy metrics Theory, seminar.
Say the Harvard MIT seminar
and I'm thinking what problem is this guy solving who has this?
This problem and you know,
sometimes there's an embarrassing silence if I ask
or there might be a fairly contrived scenario.
I want to see where the tool is useful.
There are some purely foundational tools.
I do take the point, you know, there are people who are
working on conceptual foundations of you know,
it's more becomes more like mathematical statistics.
I mean, I remember an early example
I believe that that I,
you know, I struggled to understand was the idea of stochastic Equity continuity,
which my one of my thesis advisors Whitney knew he was using to great effect and
I was trying to understand that and there isn't really.
It's really foundational. It's not but an application that's driving that
at least not immediately
but but most things are not like that and so there should be a problem.
And the I think it's on the it's on
On the, the seller of that sort of thing,
you know, because there's opportunity cost the time and attention and effort
to understand things to, you know, it's on the seller to say.
Hey, I'm solving this problem
and and here's a set of results that show that it's useful.
And here's some insight that I get.
As you said, Josh, great,
sort of there's been a move in the direction of thinking more about causality
in economics and empirical work in economics,
any consequences of sort of the Wilds,
the spread of that view. That surprised you or anything.
UB was downsides of sort of the way that he could empirical, economics has gone
sometimes.
I see somebody does Ivy and they get a result which seems implausibly large.
That's the usual case.
So it might be, you know,
an extraordinarily large causal effect of some relatively minor Intervention,
which was randomized or for which you could make a case that
that there's a good design. And then when I see that,
That and,
you know, I think,
you know,
it's very hard for me to believe that this relatively minor intervention has such
a large defect,
the author. Well, sometimes resort to the local average,
treatment effects theorem and say,
wow, these compliers, you know, they're special in some way.
And, you know, they just benefit extraordinarily from this intervention
and I'm reluctant to take that at face value. I think, you know,
often when effects are too big,
it's because the exclusion restriction is failing. So
Don't really have the right endogenous variable to scale that result.
And so I'm not too happy to see
you know, just sort of a generic heterogeneity
argument being used to excuse something that I think might be a deeper problem.
I think it played somewhat of an unfortunate roll pin.
The discussions kind of between reduced form and structural approaches where
I feel that wasn't quite
right. The instrumental variables assumptions are
at the core structural assumptions about Behavior. They were coming from economic
thinking about the economic behavior of agents,
and it's somehow it got pushed in a Direction.
That I think wasn't really very helpful. If
the way I think, initially the
we wrote things up. It was it was describing what was happening, there was set of
methods. People were using be clarified what those methods were doing
and in a way that I think contain a fair amount of insight,
but it somehow it got pushed into a corner that I think was not necessarily very
or even just the language of reduced form versus structural.
I find kind of funny in the sense that the right
the local average treatment effect model, right?
The potential outcomes model is a nonparametric.
Structural model,
if you want to think about it, as you sort of suggested, he does.
So, there's something,
there's something a little funny about putting these two things in a position when
yes, well, that language, of course, comes from the area, the
70s equations framework that we inherited.
It has the advantage that people seem
to know what you mean when you use it, but might
That people are hearing different. Different people are hearing different things.
Yeah. I think I think veggies Farmers had become use
a little bit of the pejoratives. Okay? Yeah.
The word, which is not really quite what it was originally intended for.
I guess something else that strikes me in thinking about the effects of
the local average treatment effect framework is that often folks will appeal to
a local average, treatment effects intuition for settings. Well, beyond
ones, where any sort of formal results has actually been
Shhhhht. And I'm curious given all the work that you guys did to,
you know, establish late results in different in different settings. I'm curious
any thoughts on that. I think there's going to be a lot of cases where
the intuition does get. You get you some distance,
but it's going to be somewhat limited and establishing formal results. There
may be a little tricky and there may be only work in special circumstances,
you need.
And you end up with a lot of formality that may not quite capture the intuition
sometimes I'm somewhat uneasy with them and they are not necessarily the papers.
I would want to ride that the
but I do think something do intuition orphaned US capture part of the
of the problem.
I think, in some sense we were kind of very fortunate there
in the way. The late paper go handle. It. Don't know if that, actually the editor,
made it much shorter
and that then allowed us to kind of focus on very clear, crisp results
where if, you know, this,
this is somewhat unfortunate tendency in the commercialization of having the papers.
Well, you should be able to fix that, man. I'm trying to take some time to fix that.
I think this is an example where it's sort of very clear that having it. Be sure.
It's actually impose that no paper can be longer than the late paper that wow.
Great. At least no Theory. No Theory Pig. Yeah, and I think, I think they're well,
I'm trying very hard to get the papers to be shorter.
And I think there's a lot of value
today because it's often the second part of the paper that doesn't actually
Get you much further and understanding things
but and it does make things much harder to read and, you know,
it sort of goes back to
how I think he kind of a trick should be done to you should focus on the see.
It should be reasonably close to empirical problems.
They should be very clear problems.
But then often the the theory doesn't need to be quite so long.
Yeah,
I think they had things have
On a little off track.
The relatively recent change has been a
seeming big increase in demand for people with sort of econometrics.
Causal effect, estimation skills in the tech sector.
I'm interested either of you have thoughts on sort of how that's gonna
how that's going to interact with the development of empirical methods,
or Empirical research, and economics. Going forward, sort of
whether sort of a meta point, which is there's this new kind of employer
the Amazons and the Uber and, you know,
Riser world
and I think that's great.
And I'd like to tell my students about that, you know, especially at MIT.
We have a lot of computer science Majors. That's our biggest major
and I try to seduce some of those folks into economics by saying, you know,
you can go work for these,
you know companies that people are very keen to
work for because the work seems exciting,
you know that the skills that you get in econometrics are are as good or better.
Better than than any competing discipline has to offer. So you should at least
take some econ, take some econometrics. And some econ.
I did a fun project with a uber
on labor supply of Uber drivers and was very, very exciting to be part of that.
Plus. I got to drive for Uber for a while
and I thought that was fun tonight. I did not make enough that I was attempted to
give up by a mighty job, but I enjoyed the experience.
I see a
Cho challenge to our model of graduate education here,
which is if we're trading people to go work at Amazon, you know,
it's not clear. Why? You know, we should be paying graduate stipends for that.
Why should the taxpayer effectively
be subsidizing? That our graduate education in the u.s. Is generously subsidized?
Even in private universities. It's ultimately there's a lot of public money.
Me there. And I think the traditional rationale for that is,
you know, we were training, Educators and Scholars, and there's a great externality
from the work that we do.
It's either the research externality, or a teaching externality.
But, you know, if many of our students are going to work in the private sector,
that's fine, but that maybe their employers should pay for that.
He says, so different from people working for a Consulting.
Trust me.
It's not clear to me that the number of jobs in academics has changed.
It's just, I feel like this is a growing sector whereas Consulting,
your right to raise that, it might be the same for for Consulting.
But this,
you know, I'm placing more and more students in these businesses.
So, it's on my mind in a way that I've sort of,
you know, not been attentive to consulting jobs, you know, Consulting was always,
It's important and I think they'll so there's some movement from Consulting back
into research. It's a little more fluid.
The,
a lot of the work in the
in both domains. I have to say, it's not really different but
you know, people who are working in the tech sector are doing things
that are potentially of scientific interest, but mostly it's hidden.
Then you really I have to say, you know, why, why is the government paying for this?
Yeah, although yeah, I mean taquitos point, I guess it. There's a, there's a data.
Question here of it has the sort of total nanak. It sort of say
private
for-profit sector employment of econ Ph.D.
Program graduates increased or has it just been a substitution from
finance and Consulting towards tack.
I may be a reaction to something that's not really happening
so bad. I've actually done some work with some of these tech companies.
So I don't disagree with Justice point that we need to think
a little bit about the funding model whose it was in the end paying for the
It education. But from a scientific perspective.
The only do these places have have great data and nowadays.
They tend to be very careful with that for privacy reasons,
but also have great questions.
I find it very
inspiring kind of to listen to
the people there and kind of see what kind of questions they have
and often their questions.
That also come up outside of these.
These companies have a couple of papers with the rights in the chat.
And then
as soon as an atheist kind of where we look at ways of combining experimental data
and observational data, and can it there.
Rights Chetty was interested in what is the effect
of Early Childhood programs on outcomes later in life? Not just kind of test scores,
but on earnings and stuff, and we cannot be developed methods
that would help you shed light on that, on the some,
in some settings and the same problems.
Came up kind of in this tech company settings.
And so for my perspective, it's
the same kind of a stocking two people doing a protocol work.
I tried to kind of look at these specific problems and then try to come up
with more General problems that we formulating the problems at a higher level.
So that I can think about solutions that work in a range of settings.
And so from that perspective, the
His with the the tech companies I just very valuable and very useful.
It's know.
We do have students. Now spent
doing internships there and then coming back and writing
more interesting thesis, as a result of their experiences there.
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If you'd like to learn more about he do.
Josh and Isaiah check out the links in the description.